Abstract
Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as “building” by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are “building” in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort.
Highlights
The creation and maintenance of databases of buildings have numerous applications, which involve urban planning and monitoring as well as three-dimensional (3D) city modeling.In particular, the complete documentation of buildings in official cadastral maps is essential to the transparent management of land properties, which can guarantee the legal and secure acquisition of properties
In Germany, the boundary of a building is acquired through a terrestrial survey by the official authority and a two-dimensional (2D) ground plan of buildings is documented in the official cadastral map, which is known as the digital cadastral map (DFK)
In order to evaluate the Convolutional Neural Network (CNN) performance of the proposed framework, we compare our building detection results in the district of Bad Toelz with those acquired from two conventional solutions utilized in the state of Bavaria, Germany
Summary
The creation and maintenance of databases of buildings have numerous applications, which involve urban planning and monitoring as well as three-dimensional (3D) city modeling.In particular, the complete documentation of buildings in official cadastral maps is essential to the transparent management of land properties, which can guarantee the legal and secure acquisition of properties. Due to the lack of information from owners about some building construction projects, some building constructions are never recorded via terrestrial surveying and are missing in the DFK. New undocumented buildings are buildings that have only recently been erected In this regard, the building ground plans of both old and new undocumented buildings are missing in the DFK. Undocumented story construction will not lead to changes in the DFK, but this information is crucial to updating 3D building models. Collecting this undocumented building constructions is necessary to continue and complete these databases
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
